Systems and methods for predicting environmental conditions

ABSTRACT

Provided are systems and methods for a predictive analysis system, comprising: at least one first sensor at a first location of interest that receives a first source of sensor data; at least one second sensor at a second location of interest that receives a second source of sensor data; and a predictive data processing device that generates a predictive outcome regarding an anticipated event at the first location of interest in response to an analysis of a combination of the first source of sensor data, the second source of sensor data, and a source of historical data.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 62/950,221, filed on Dec. 19, 2019 entitled “SENSORS FOR PREDICTIVEOUTCOME AND MAINTENANCE AND SMART CONTROL,” the entirety of which isincorporated by reference herein.

FIELD OF THE INVENTION

The inventive concepts relate generally to data collection for providinga predictive outcome. More specifically, the inventive concepts relateto systems and methods that collect data from one or more geographiclocations, analyze the data to predict environmental and air qualityconditions and to notify users of a predictive outcome, and alter anoutput and/or performance of electronic and mechanical devices based onthe analyzed data.

BACKGROUND

As modern technology continues to provide improvements in datacollection, the manner in which the data is used can be challenging invarious applications. For example, sensing technologies can be found todetect the presence of hazardous gas or chemicals. However, a needexists for data collection used to predict with accuracy the possibilityof a gas or chemical hazard so that an action can be proactively takento prevent or mitigate the risk of the hazard from occurring.

SUMMARY

In one aspect, provided is a predictive analysis system, comprising: atleast one first sensor at a first location of interest that receives afirst source of sensor data; at least one second sensor at a secondlocation of interest that receives a second source of sensor data; and apredictive data processing device that generates a predictive outcomeregarding an anticipated event at the first location of interest inresponse to an analysis of a combination of the first source of sensordata, the second source of sensor data, and a source of historical data.

In some embodiments, the predictive outcome controls a machinepotentially impacted by the anticipated event.

In some embodiments, the anticipated event is a chemical or gas hazard.

In some embodiments, the first sensor collects a real-time actualenvironmental condition at the first location of interest as the firstsource of sensor data, and the second sensor collects information thatis a possible impact on the first source of sensor data.

In some embodiments, the predictive analysis system further comprises ananalytics computer that is trained to generate an analytics input to thepredictive data processing device in response to performing the analysisof the combination of the first source of sensor data, the second sourceof sensor data, and a source of historical data.

In some embodiments, the predictive data processing device receives thefirst source of sensor data as raw data and compares the raw data to thehistorical data which includes previously collected sources of data fromthe at least one first sensor to generate the predictive outcome.

In some embodiments, the predictive outcome is constructed and arrangedfor modifying a displayed object replica to include the predictiveoutcome.

In another aspect, a predictive data processing device comprises a firstinput that receives a first source of sensor data from at least onefirst sensor at a first location of interest; a second input thatreceives a second source of sensor data from at least one second sensorat a second location of interest; a third input that receives a sourceof historical data; and a special-purpose processor that generates apredictive outcome regarding an anticipated event at the first locationof interest in response to an analysis of a combination of the firstsource of sensor data, the second source of sensor data, and the sourceof historical data.

In some embodiments, the predictive outcome controls a machinepotentially impacted by the anticipated event.

In some embodiments, the anticipated event is a chemical or gas hazard.

In some embodiments, the special-purpose processor is furtherconstructed and arranged to process an analytics input that includestrained machine learning data generated in response to an analysisperformed on the first source of sensor data, the second source ofsensor data, and a source of historical data.

In some embodiments, the first input receives the first source of sensordata as raw data and compares the raw data to the historical data whichincludes previously collected sources of data from the at least onefirst sensor to generate the predictive outcome.

In some embodiments, the predictive outcome is constructed and arrangedfor modifying a displayed object replica to include the predictiveoutcome.

In another aspect, a system that predicts air quality at a locationcomprises at least one gas or chemical sensor at a first location ofinterest that receives a first source of sensor data; at least one thirdparty sensor at a second location of interest that receives a secondsource of sensor data; and a predictive data processing device thatgenerates a predictive outcome regarding a possible gas or chemicalhazard at the first location of interest in response to an analysis of acombination of the first source of sensor data, the second source ofsensor data, and a source of historical data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a network diagram illustrating an environment in whichembodiments of the present inventive concepts can be practiced.

FIG. 2 is a schematic diagram illustrating a single-location system forpredicting environmental conditions, in accordance with someembodiments.

FIG. 3 is a block diagram of a predictive data processing device, inaccordance with some embodiments.

FIG. 4 is a flow diagram illustrating a method for predicting anenvironmental condition, in accordance with some embodiments.

FIG. 5 is a flow diagram illustrating a method for electronic digitalreplication and display of an object impacted a real or predictedenvironmental condition, in accordance with some embodiments.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In the following description, specific details are set forth although itshould be appreciated by one of ordinary skill that the systems andmethods can be practiced without at least some of the details. In someinstances, known features or processes are not described in detail so asnot to obscure the present invention.

Conventional sensing technologies can predict chemical hazards bydetecting chemicals, gasses, from various sensors positioned in variouslocations of a facility. However, predictive analysis is based on thesensor data alone.

In brief overview, systems and methods in accordance with embodiments ofthe present inventive concepts collect and process data concerningvarious environmental conditions to generate a predictive outcome. Inaddition, the systems and methods can provide an output to other objectsrelated to the environmental conditions that may be otherwisepotentially affected by the predictive outcome for changing a status ofthe other objects.

For example, the inventive concepts can collect data from any device,equipment, sensor, or other physical object at one or more differentlocations concerning air quality and provide a predictive outputconcerning the risks of gas and/or chemical hazards or the possibilityof a fire or explosion in real-time or near real-time. The predictiveoutput, analysis, or maintenance result is established from differentdata provided by various data sources, one of which can be the sensordevices, that is processed and executed by an algorithm that generatesprovide the output and provide status changes to different objects inthe environment.

Embodiments of the present inventive concepts may to other industriesand applications such as but not limited to manufacturing facilities,petrochemical facilities, smart cities or smart homes, commercial orresidential buildings, parking lots, or other indoor or outdoorlocations, food and agriculture, unmanned autonomous vehicles such asdrones or self-driving vehicles, robotic platforms, and so on that mayparticipate in environments susceptible to forces of nature or othermanmade or natural acts.

FIG. 1 is a network diagram illustrating an environment in whichembodiments of the present inventive concepts can be practiced.

Although FIG. 1 illustrates two locations 10, 20, other embodiments caninclude one or more locations 10, 20. A location 10, 20 may be abuilding or outdoor location. In some embodiments, a location 10, 20 maybe a region of a machine such as a robotic apparatus or automobile. Insome embodiments, a location 10, 20 may a container or cargo that storesobjects that produces gas, chemicals, or other emissions detectable by asensor 102, 103. In some embodiments, a location 10, 20 can include aphysical layout that includes one or more sensors 102, 103 thatexchanges data with a communication hub 104 via a wired or wireless datalink. In some embodiments, one or more sensors (generally, 102, 103) arenanotechnology-based sensors. In other embodiments, one or more sensorsare microsensors or picosensors, for example, sensor arrays for nano-and pico-fluidic systems. In some embodiments, elements 102, 103 includeInternet of Things (IoT) devices or the like for communicating with thecommunication hub 104, database 106, and/or predictive data processingdevice 107. In some embodiments, sensors 102 are gas and/or chemicalsensors and sensors 103 are sensors that provide contextual data withrespect to data collected by the gas and/or chemical sensors 102, suchas wind speed and so on. Accordingly, sensors 102 may be referred to a“first sensors” and sensors 103 may be referred to as “second sensors.”Although gas, chemical IoT devices and/or sensors are described, othersensor types for detecting objects, forces, substances, gasses, liquids,and/or solids of interest may equally apply and converting the detectionresults into electrical signals which can be formatted into data forprocessing by other elements of the system.

The communication hub 104 in turn can output data collected by thesensors 102, 103 to a remote storage device such as one or moredatabases 106 via a network 16. The network 16 can be a local areanetwork (LAN), a wide area network (WAN), or other communicationsnetwork for transmitting electronic data. In some embodiments, network16 may include cloud computing systems, data center computers, or thelike which may include the remote storage device 106 and/or otherprocessor-based devices shown and described in FIG. 1. In someembodiments, a data center of the network 16 may be a private, public,or semi-public hybrid data center that receives and processes data fromone or both locations 10, 20, and/or data from other sources used by thepredictive data processing device 107 to generate an environmentalcondition prediction result.

The remote storage device 106 may include one or more databases thatstore incoming raw data and/or formatted data 121 from various sources.For example, raw data 121 may refer to data that is native orproprietary with respect to a supported data format of the objectproducing the data. Formatted data on the other hand may refer to rawdata that has been converted by a special-purpose computer processor toa different format that is supported by a processor different than thatof the originating object, for example, an algorithm embodied in programcode that is executed by the predictive data processing device 107. Insome embodiments, the incoming data 121 may originate from one or moregas and/or chemical sensors 102 that provide readings of gasses and/orchemicals in the air at the location 10, 20, for example, measured inparts per million (PPM). In some embodiments, a database of the remotestorage device 106 receives and stores gas and/or chemical sensor data121 that can be processed for comparison to other raw data and/orformatted data. For example, other sensors 103 may include winddirection and speed sensors that provide wind conditions at a location10, 20 where gasses and/or chemicals are detected. In addition to thegas and/or chemical sensor data 121 (in raw or new format), third partyor other sensor data, e.g., wind condition data 122 may be received fromthe include wind direction and speed sensors 103 and stored at theremote storage device 106. In some embodiments, the database receivesand stores other data 123 from objects such as local and nationalweather data, video, static image of equipment, start and end times of adetected source of wind, etc. This other data 123 can be used incombination with gas and/or chemical sensor data to predictenvironmental conditions. In some embodiments, such data 123 may begenerated from a learning algorithm or the like executed by anartificial intelligence computer 13.

In some embodiments, a predictive data processing device 107 processes acombination of sensor and contextual data inputs to present an outcomevalue 124 to a user. In some embodiments, the outcome value 124 isgenerated by a machine learning algorithm embodied in program code thatis executed by the predictive data processing device 107, and thatgenerates the outcome value 124 in response to a combination of thestored data sets 121, 122, and 123. For example, a gas sensor 102 mayprovide a ppm value that is processed with other contextual data , e.g.,wind and speed sensor data 122 from sensors 103 and stored historicalweather condition data 123 to generate a predictive outcome information124 that is formatted for display on a computer monitor or the like forviewing by a user. In some embodiments, an outcome value 124 isgenerated by comparing gas and chemical data from different data sourcesto incoming raw or formatted data. In this case, the system may detect acompound that is not recognized by an IoT device 102. The predictivedata processing device 107 takes the incoming sensor data as raw dataand compares it to other previously stored raw data, for example,previous sensor results taken in benign conditions at the location 10,20. In some embodiments, the generated values 124 are stored at thedatabase 106 for subsequent processing. The predictive data processingdevice 107 may store and execute program code that processes the datausing outside data and factors. Outside external factors may apply tocollected data received at predictive data processing device 107, forexample, external environmental conditions that are unrelated to thoseconditions at a location of interest, but can nevertheless be combinedwith other received data to predict an outcome, i.e., generate apredictive outcome information 124. For example, a third-party sensorexternal to location 10, for example, at location 20 may detect a sourceof pollen. Other data 122, 123 may provide information that windconditions flow from location 20 to location 10. The predictive dataprocessing device 107 can generate a predictive outcome information 124that the pollen will arrive at location 10 at an estimated time based onthis combination of data.

One or more machines 110 may be at a location 10, 20, near a location10, 20, or otherwise in electronic communication with the electroniccomponents such as the sensor(s)102, 103 and/or communication hub 104 ofthe one or more locations 10, 20. In some embodiments, a machine 110includes a computer network interface that communicates via the network16 according to a communication protocol such as TCP/IP, wired orwireless, 3G, 4G, 5G, satellite, and so on. A machine 110 may includebut not be limited to mechanical equipment for manufacturing forproducing items such equipment, chemical and gas processing units, solidmaterial processors that evaporates gas and chemical in the air, foodprocessing machinery, and so on. Accordingly, some embodiments of theinventive concepts include the presence of sensors 102, 103 at variouslocations 10, 20, but the sensor data is not processed at the locations10, 20 but is instead output via the network 16 to the predictive dataprocessing device 107 located at a different centralized location, suchas a cloud computing environment or other remote location. A machine 110can also include sensors 102 and/or 103 for collecting sensor data to becompared with the data at locations 10, 20. In some embodiments, themachine 110 has at least one hardware computer processor for exchangingdata with the predictive data processing device 107 and/or othercontrollers for performing an operation in response to an environmentalcondition prediction result generated by the predictive data processingdevice 107.

An example machine 110 may be a tractor that spreads a fertilizer abouta farm. The sensor 102 on the tractor 110 may detect the presence of adangerous chemical in the fertilizer. This sensor data can be comparedat the predictive data processing device 107 to historical dataregarding the source of fertilizer used in addition to real-time sensordata from a location 10 where the fertilizer is produced to establishthat a risk is possible that the fertilizer plant 10 is producingfertilizer with a high quantity of the dangerous chemical.

In addition, or alternatively, the environment described in FIG. 1according to some embodiments may include objects other than the machine110 such as gas or chemical pipelines 111 or the like, a roboticplatform 112, a manual or unmanned vehicle 113 having a battery that isprone to environmental hazards, and /or other physical objects. Objects110-113 may be configured with a sensor similar to or different than thesensors 102, 103 at the locations 10, 20. In some embodiments, thepredictive data processing device 107 can output a computer command toone or more of the machines 110-113 to control or adjust their outputand performance according to data, such as sensor data received by thepredictive data processing device 107.

FIG. 2 is a schematic diagram illustrating a single-location system forpredicting environmental conditions, in accordance with someembodiments. Although FIG. 2 describes a single location 10, otherembodiments can include multiple locations, for example, both locations10 and 20 described in FIG. 1.

The location 10 may include at least one gas sensor 102A and at leastone chemical sensor 102B that detect in real-time a gas and/or chemicalat the location, convert the detection results into electronic data, andoutput (202) the electronic sensor data via a communication hub 104 tothe network 16. The communication hub 104 can communicate via the datalink and/or network 16 using one or more different communicationprotocols and/or related technologies such as but not limited toEthernet, TCP/IP, message queue telemetry transport (MQTT) virtualprivate network (VPN), radio frequency (RF), Bluetooth™, satellite,and/or other data network communication between the IoT sensor 102 andthe hub 104. The communication hub 104 can in turn output (204) the datato the database 106 and/or predictive data processing device 107 usingone or more network computing technologies such as Zigbee, Zwave, LoRan,Wi-Fi, 3G, 4G, 5G technology, satellite or the like to the network 16 toa network cloud or other location, for example, a location that situatesthe database 106 and predictive data processing device 107 using publicinternet and/or private links. The communication hub 104 may communicatewith the database 106 to store the sensor data. The communication hub104 may communicate with the predictive data processing device 107 toprovide the sensor data to the predictive data processing device 107 insituations where the database 106 does not receive the sensor data.

The predictive data processing device 107 communicates (206, 208, 210)with the database 106 and/or the other data sources 110-113 to analyzethe inputs generated (210, 211, 212) from collected sensor data, e.g.,data 121 gas/chemical sensors 102 and context information 122 fromsensors 103, and historical data 123. An outcome value 124 can begenerated by the predictive data processing device 107, and output (214,218) to a user computer 11, 13. For example, a first communicationconnection (206) may include a data exchange between the predictive dataprocessing device 107 and the database 106 to process stored sensor data121-124, for example, a combination of stored historical environmentdata concerning weather patterns at a location 10, sensor data 121concerning traces of a gas or chemical currently at the location, andsensor data 122 concerning a current temperature or wind speed detectedat the location 10. Some or all of this data, such as sensor data 121,122 may be received directly by a second communication connection (208)from the location 10 by instead of or in addition to the database 106.In some embodiments, some or all of this data is received from amechanical device 110-113. A third communication connection (210) mayprovide other data, such as raw and/or formatted data, for comparison bythe predictive data processing device 107 to the actual sensor datacollected at the location. A fourth communication connection (212) mayprovide other collected data 123 in real time or near real time that isalso processed by the predictive data processing device 107 to generatea predictive outcome value 124.

The predictive outcome value 124 can be generated in one or moredifferent formats, and can include different data depending the type ofoutput of the predictive data processing device 107. A first output(214) can be output to a user, in particular, a user computer 11 havinga display or other output devices for providing data related to thepredictive outcome value 124 in a visual, audio, and/or tactile manner.For example, the outcome value 124 may be converted by the predictivedata processing device 107 to a format that permits the user to know atan early stage of a predictive outcome such as possible air qualityissues, chemical leakage, or an accident, plus the time will take forthe area at the location 10, 20 becomes dangerous zone. In someembodiments, the first output (214) provides an action directly tousers. For example, a user is provided with set of instructions toprevent the outcome or protect the user. Such an action could include aninactivation or a shutdown of specific machinery, repairing specificmachinery, or generating a time estimate regarding the environmentalconditions at the location. In the latter example, the predictiveoutcome value 124 may be in the form of an electronic text message orother data communication that notifies a recipient of an imminent dangerregarding possible high radiation levels at the location 10 due to thepredictive data processing device 107 receiving data concerning windconditions, high radiation levels detected at a nearby location,analytic processing, and so on. According to the processing result, theuser may be further notified that persons at the location 10 may be safefor 1 hour before the radiation reaches the location 10.

A second output (216) can be a computer command or the like to otherdevices to alter their output and performance. For example, the secondoutput (216) may include a status change request that is output to thelocation 10 where gas or chemical is detected. In this example, a carbondioxide sensor 102A may sense a presence of a low but not dangerouslevel of carbon dioxide. However, a second sensor at a furnace 118 atthe location may detect a gas flow pipe 111 that is fragile and at riskof forming a hole from which carbon dioxide may possibly escape. Thiscollection of data may be processed whereby the predictive dataprocessing device 107 outputs (216) an electronic request directly tothe furnace 118 including an instruction to automatically inactivate thefurnace 118.

A third output (218) can include the predictive outcome value 124 in aformat suitable for receipt and processing by a third-party analytics,machine learning, deep learning, or other artificial intelligencecomputer 13, and so on. The computer 13 may contribute to training the pinputs at various times to train the predictive data processing device107 with respect to machine learning, deep learning, and the like, forexample, where the computer 13 deploys a trained AI model.

FIG. 3 is a block diagram of the predictive data processing device 107described in FIGS. 1 and 2. The predictive data processing device 107includes a processor such as a CPU 22, a memory 24, and input/output(I/O) logic 32, for example, a network interface card (NIC), whichcommunicate with each other via a data/control bus and/or data connector25, for example, a peripheral component interconnect (PCI) bus. The I/Ologic 32 can include one or more adaptors for communicating with thenetwork 16.

The memory 24 can include volatile memory, for example, random accessmemory (RAM) and the like, and/or non-volatile memory, for example,read-only memory (ROM), flash memory, and the like. The memory 24 caninclude removable and/or non-removable storage media implemented inaccordance with methods and technologies known to those of ordinaryskill in the art for storing data. Stored in the memory 24 can includeprogram code, such as program code of an operating system (OS) 28executed by the processor 22.

The memory 24 also includes a predictive output system 26, whichreceives and processes via the I/O logic 32 one or more data inputs 121,122, 123 described herein, and generates a predictive output 124described herein. The predictive output system 26 may include acorrelation analyzer 27 that correlates captured sensor data with otherdata captured from the same geographic area 10, 20 and/or nearby area toproduce a prediction result 124. In some embodiments, the correlationanalyzer 27 includes artificial intelligence technology or the like forcommunicating with the artificial intelligence or machine learningcomputer 13, for example, so that the computer 13 can execute a learningalgorithm, or simulation e.g., for AI training with respect tocontributing to the generation of the predictive output 124.

FIG. 4 is a flowchart illustrating a method 400 for predicting anenvironmental condition, in accordance with some embodiments. Some orall of the method 400 can include steps performed in electroniccomponents described in FIGS. 1-3. For example, some or all of themethod 400 may comply with an algorithm embodied in program code that isexecuted by the predictive data processing device 107 described in FIGS.1-3, for example, performed by the predictive output system 26 in FIG.3.

At block 401, a set of first data is received from one or more sensors102 at a location.

At block 402, a set of second data is received from one or more sourcesthat are external to the location where the sensors 102 are located.

At block 403, a predictive output is generated from a combination of thefirst and second data, which in turn is generated by different sources,sensors, historical data and real-time data. In some embodiments, thepredictive output includes data about the possibility of a gas,chemical, fire, explosion, or other hazard in real-time or nearreal-time.

At block 404, the predictive output is applied to one or more machines.Method 400 can apply to various applications. For example, as describedherein, some embodiments include a location 10 that includes at leastone chemical sensor 102B that detect a chemical. Here, the predictiveoutcome value 124 determined from the detection of chemicals of thesensor 102B may include data corresponding to a chemical hazard outbreakprediction at the location 10, or a different geographic area orenclosed area. The chemical hazard outbreak predictive output 404 mayinclude time left to reach a symptoms level, time left to reach acritical level, and/or other information used to provide time-relatedprediction information. Another predictive output 404 may pertain to thedirection that a hazardous chemical is traveling based on collectedinformation from the location sensors or other sources such as onlinemeteorological data such as temperature, wind direction, and wind speed.

In a related example, equipment performance may result in an increase inair pollution at the location 10. The predictive output 404 may providean assessment on how the location 10 may be affected based on thecollected information from the location sensors or other sources such asonline meteorological data such as temperature, wind direction, and windspeed. The predictive output 404 may be output as electronic signals,e.g., data, to the equipment that controls the equipment to performdifferently to reduce the air pollution, for example, reduce a speed ofoperation which in turn results in less emissions by the equipment.

In another example, the method 400 may be applied to gas or chemicalleaks in a petroleum pipeline 111 (see FIG. 1) so that a predictiveoutput 404 predicts a speed or quantity of a given pipeline leak. Forexample, known historical data may establish that carbon monoxide levelsat the pipeline 111 are at a threshold level. However, sensors 102 maydetect a current carbon monoxide level that is greater than thethreshold level. The system can output instructions to the sensors 102to collect carbon monoxide readings along the pipeline 111 to determinethe location of a leak causing the increased carbon monoxide readings.Wind conditions can be determined from second sensors 103 and used bythe predictive data processing device 107 to predict that the highreadings are downstream from the actual location of the leak.

In another example, the method 400 may be applied to a battery topredict an operating lifespan of the battery by correlating gas orchemical leaks and the age of the battery.

In another example, the method 400 may detect the composition of achemical of interest using artificial intelligence technology, forexample, artificial intelligence computer 13, as well as the first andsecond data of steps 401 and 402 respectively. For example, historicaldata such as safe levels of the chemical of interest may be used totrain the artificial intelligence computer 13, for example, processedwith data regarding current levels of the chemical of interest forgenerating a predictive output 404. The system can therefore rely onhistorical data instead of feedback when generating the predictiveoutput.

In another example, the method 400 may be applied in the food industryand agriculture. For example, a sensor 102 may detect the odor of fruit,or an analyte sensor may predict if the food cooked correctly correlatedwith cooking time and temperature. This sensor data can be processed sothat the system 107 predicts the time left to stay viable and freshwhile in storage or transport.

In another example, the system 107 may process environment data andpredict the shelf time for produce, livestock, and dairy.

FIG. 5 is a flow diagram illustrating a method 500 for electronicdigital replication and display of an object impacted a real orpredicted environmental condition, in accordance with some embodiments.Some or all of the method 500 can include steps performed in electroniccomponents described in FIGS. 1-3. For example, some or all of themethod 500 may comply with an algorithm embodied in program code that isexecuted by the predictive data processing device 107 described in FIGS.1-3, for example, performed by the predictive output system 26 in FIG.3. Some or all of the method 500 may be executed on a computer display.

As described above, embodiments may include a database 106 that stores acombination of raw data and/or formatted data 121 and other sensor data122 from various sources, historical data 123, and predictive outcomeinformation 124. At block 501, this data may be output from the database106. At block 502, the data output from the database 106 may beformatted for a display screen, referred to as a first format. Forexample, the data may be an outcome value 124 generated by thepredictive data processing device 107, in the form of values, charts,text information, or the like that notifies a reader of a predictiveevent, for example, described by way of example in a foregoingembodiment.

At block 503, the data output from the database 106 may be formatted asa display replica of an object to which a predictive event corresponds,referred to as a second format. The object to be displayed may be amachine 110 or other object 111-113 shown and described with regard toFIGS. 1-4. A two-dimensional or three-dimensional image orrepresentation of the object is displayed on a computer display. Thedisplay replica can be generated by a computer graphics system or otherwell-known electronic apparatus for generating computer graphics. Alongwith the display replica of the object, a visual, audio, or tactilemarker can be generated that identifies a location of a predictiveevent, for example, location on a pipeline 111 where a crack may bedetermined based on a predictive result generated by the predictive dataprocessing device 107 due to a sensed increase in gas emitted from thepipeline.

In some embodiments, the display replica is generated by aspecial-computer software application constructed and arranged tocommunicate with a computer display such as a monitor to display theoutcome on a digital replica of living and non-living physical entities,e.g., human body, machinery, city, and building, for example, atlocation 10 or 20. A three-dimensional replica can display the outputwithin the replica. For example, if a fan of machinery is reporting anissue, it will turn the color of the fan red. The user will have theability to manipulate the replica but digital removing other attachedparts to see another area on the replica. The user using input devices,keyboards, touch screen or hand gestures to simulate the replica bysending new input to the replica and observe the outcome using thereplica. The new input data will be sent to the predictive applicationand run. It is processed and provides the output on the replica.

Furthermore, a replica will display an output of a simulation thatdisplays the output from a combination of real-time data, historicaldata, and other related data used by the system to generate thepredictive output.

At block 504, a user can modify the display replica, for example, usinga mouse or other input/output device to select the visual mark to zoomin on a region of the display replica. For example, an object replicamay be a map of a location 10 or a three-dimensional representation of apipeline 111. A user can modify the display to zoom in on a map of thelocation 10 or remove a layer of the displayed representation of thepipeline to identify a specific region of a predictive event, such as alocation of a gas leak.

At block 505, a user can use a display replica to control a machine orobject. For example, an automobile may be running in a garage or otherenclosed area, thereby generating high carbon monoxide levels. However,a carbon monoxide detector in a second floor bedroom has not yetdetected carbon monoxide. The predictive data processing device 107 maygenerate a predictive outcome value 124 regarding the imminent arrivalin the second floor bedroom of the carbon monoxide and notify theoccupants of this predictive event. A user can view a digital displayreplica of the automobile and select the ignition switch displayed onthe automobile to shut the automobile off. Other examples may includethe ability to control a machine 110 by touching the displayed object toalter a state of the machine, increase or decrease a function of themachine, and so on.

At block 506, a new machine status can be generated and output to thepredictive database 106, for example, as stored data 124. The predictivedata processing device 107, which in some embodiments may be part of thedatabase 106, can generate a new output based on this new machine statusdata. An example of an operation that includes this new generated datamay be that the replica of a machine indicates that the status of allmachine functions is satisfactory, and the elements of the machine arecorrectly operating. The generated output can be output to and stored atthe database 106.

As previously described, a computer system described with reference tothe figures herein may generally comprise a processor, an input devicecoupled to the processor, an output device coupled to the processor, andmemory devices each coupled to the processor. The processor may performcomputations and control the functions of the system, includingexecuting instructions included in computer code for the tools andprograms capable of implementing methods for allocating trailers andloading docks, in accordance with some embodiments, wherein theinstructions of the computer code may be executed by the processor via amemory device. The computer code may include software or programinstructions that may implement one or more algorithms for implementingone or more of the foregoing methods, techniques, algorithms, and thelike. The processor executes the computer code.

The memory device may include input data. The input data includes anyinputs required by the computer code. The output device displays outputfrom the computer code. A memory device may be used as a computer usablestorage medium (or program storage device) having a computer-readableprogram embodied therein and/or having other data stored therein,wherein the computer-readable program comprises the computer code.Generally, a computer program product (or, alternatively, an article ofmanufacture) of the computer system may comprise said computer usablestorage medium (or said program storage device).

As will be appreciated by one skilled in the art, in a first embodiment,the present invention may be a method; in a second embodiment, thepresent invention may be a system; and in a third embodiment, thepresent invention may be a computer program product. Any of thecomponents of the embodiments of the present invention can be deployed,managed, serviced, etc. by a service provider that offers to deploy orintegrate computing infrastructure with respect to embodiments of theinventive concepts. Thus, an embodiment of the present inventiondiscloses a process for supporting computer infrastructure, where theprocess includes providing at least one support service for at least oneof integrating, hosting, maintaining and deploying computer-readablecode (e.g., program code) in a computer system including one or moreprocessor(s), wherein the processor(s) carry out instructions containedin the computer code causing the computer system for generating atechnique described with respect to embodiments. Another embodimentdiscloses a process for supporting computer infrastructure, where theprocess includes integrating computer-readable program code into acomputer system including a processor.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer-readable program instructions.

These computer-readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer-readable program instructionsmay also be stored in a computer-readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that thecomputer-readable storage medium having instructions stored thereincomprises an article of manufacture including instructions whichimplement aspects of the function/act specified in the flowchart and/orblock diagram block or blocks.

The computer-readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce acomputer-implemented process, such that the instructions which executeon the computer, other programmable apparatus, or other device implementthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

A number of implementations have been described. Nevertheless, it willbe understood that the foregoing description is intended to illustrate,and not to limit, the scope of the inventive concepts which are definedby the scope of the claims. Other examples are within the scope of thefollowing claims.

What is claimed is:
 1. A predictive analysis system, comprising: atleast one first sensor at a first location of interest that receives afirst source of sensor data; at least one second sensor at a secondlocation of interest that receives a second source of sensor data; and apredictive data processing device that generates a predictive outcomeregarding an anticipated event at the first location of interest inresponse to an analysis of a combination of the first source of sensordata, the second source of sensor data, and a source of historical data.2. The predictive analysis system of claim 1, wherein the predictiveoutcome controls a machine potentially impacted by the anticipatedevent.
 3. The predictive analysis system of claim 1, wherein theanticipated event is a chemical or gas hazard.
 4. The predictiveanalysis system of claim 1, wherein the first sensor collects areal-time actual environmental condition at the first location ofinterest as the first source of sensor data, and the second sensorcollects information that is a possible impact on the first source ofsensor data.
 5. The predictive analysis system of claim 1, furthercomprising an analytics computer that is trained to generate ananalytics input to the predictive data processing device in response toperforming the analysis of the combination of the first source of sensordata, the second source of sensor data, and a source of historical data.6. The predictive analysis system of claim 1, wherein the predictivedata processing device receives the first source of sensor data as rawdata and compares the raw data to the historical data which includespreviously collected sources of data from the at least one first sensorto generate the predictive outcome.
 7. The predictive analysis system ofclaim 1, wherein the predictive outcome is constructed and arranged formodifying a displayed object replica to include the predictive outcome.8. A predictive data processing device, comprising: a first input thatreceives a first source of sensor data from at least one first sensor ata first location of interest; a second input that receives a secondsource of sensor data from at least one second sensor at a secondlocation of interest; a third input that receives a source of historicaldata; and a special-purpose processor that generates a predictiveoutcome regarding an anticipated event at the first location of interestin response to an analysis of a combination of the first source ofsensor data, the second source of sensor data, and the source ofhistorical data.
 9. The predictive data processing device of claim 8,wherein the predictive outcome controls a machine potentially impactedby the anticipated event.
 10. The predictive data processing device ofclaim 8, wherein the anticipated event is a chemical or gas hazard. 11.The predictive data processing device of claim 8, wherein thespecial-purpose processor is further constructed and arranged to processan analytics input that includes trained machine learning data generatedin response to an analysis performed on the first source of sensor data,the second source of sensor data, and a source of historical data. 12.The predictive data processing device of claim 8, wherein the firstinput receives the first source of sensor data as raw data and comparesthe raw data to the historical data which includes previously collectedsources of data from the at least one first sensor to generate thepredictive outcome.
 13. The predictive data processing device of claim8, wherein the predictive outcome is constructed and arranged formodifying a displayed object replica to include the predictive outcome.14. A system that predicts air quality at a location, comprising: atleast one gas or chemical sensor at a first location of interest thatreceives a first source of sensor data; at least one third party sensorat a second location of interest that receives a second source of sensordata; and a predictive data processing device that generates apredictive outcome regarding a possible gas or chemical hazard at thefirst location of interest in response to an analysis of a combinationof the first source of sensor data, the second source of sensor data,and a source of historical data.